When a user interacts with a user interface or reads a printed page, the user is engaged in a range of activities, including reading, selecting, inspecting parts of an image, and so on. Some eye tracking technology can capture what a user pays attention to, or classify if a user is reading text. However, the classification processes are limited to text, and rely solely on eye movement. To get a more fine grained understanding of users' activities, a more detailed analysis is required.
One problem with eye tracking data is that it includes a lot of random variation (i.e., noise). For this reason, current eye tracking techniques do not result in accurate data. For example, current techniques are not sufficiently reliable at detecting what a person is looking at or what a person is doing.